arXiv Computer Vision September 14, 2026 By Imad Ali Shah, Imran Mehmood, Enda Ward, Martin Glavin, Edward Jones, Brian Deegan

HSI-Road Relabeled: Surface-Aware Road-Scene Segmentation

Read the original on arXiv Computer Vision →

The HSI‑Road dataset now includes manually labeled six‑class road‑scene annotations (Background, Asphalt, Concrete, Dirt, Water, Grass) and a pipeline for RGB‑to‑NIR registration. Six semantic‑segmentation models were evaluated on four input types—original‑resolution RGB, registered low‑resolution RGB, NIR, and stacked RGB‑NIR—using per‑class and mean IoU and F1 scores. Results show that high‑resolution RGB yields the best overall performance, but at matched resolution the stacked RGB‑NIR input consistently outperforms NIR alone and often surpasses registered RGB, especially for the Water class.

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